Significance-Linked Connected Component Analysis for Low Bit Rate Image Coding

Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the "Barbara" image, at 0.5 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. This outstanding performance is achieved without u..